Overview

Dataset statistics

Number of variables15
Number of observations340
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.0 KiB
Average record size in memory120.4 B

Variable types

NUM12
BOOL2
CAT1

Reproduction

Analysis started2020-08-25 01:50:02.276305
Analysis finished2020-08-25 01:50:25.964117
Duration23.69 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Variables

ID
Real number (ℝ≥0)

Distinct count86
Unique (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.6794117647059
Minimum7.0
Maximum276.0
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2020-08-25T01:50:26.009463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13
Q159
median130
Q3233
95-th percentile267
Maximum276
Range269
Interquartile range (IQR)174

Descriptive statistics

Standard deviation89.37703202
Coefficient of variation (CV)0.6636280248
Kurtosis-1.439753143
Mean134.6794118
Median Absolute Deviation (MAD)87
Skewness0.1598445916
Sum45791
Variance7988.253852
2020-08-25T01:50:26.114633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
751.5%
 
11751.5%
 
6151.5%
 
6451.5%
 
10351.5%
 
14151.5%
 
8451.5%
 
8051.5%
 
7551.5%
 
6051.5%
 
3851.5%
 
5151.5%
 
6651.5%
 
7051.5%
 
10751.5%
 
5951.5%
 
8151.5%
 
3051.5%
 
9651.5%
 
13051.5%
 
15051.5%
 
14351.5%
 
7451.5%
 
3541.2%
 
16741.2%
 
Other values (61)21763.8%
 
ValueCountFrequency (%) 
751.5%
 
830.9%
 
1141.2%
 
1241.2%
 
1341.2%
 
1841.2%
 
1941.2%
 
2041.2%
 
2141.2%
 
2241.2%
 
ValueCountFrequency (%) 
27641.2%
 
27541.2%
 
27410.3%
 
27241.2%
 
27020.6%
 
26741.2%
 
26441.2%
 
26341.2%
 
26241.2%
 
26120.6%
 

target
Categorical

Distinct count3
Unique (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1
200
0
78
2
62
ValueCountFrequency (%) 
120058.8%
 
07822.9%
 
26218.2%
 
2020-08-25T01:50:26.251388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
120058.8%
 
07822.9%
 
26218.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number340100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
120058.8%
 
07822.9%
 
26218.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common340100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
120058.8%
 
07822.9%
 
26218.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII340100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
120058.8%
 
07822.9%
 
26218.2%
 

gain_ratio_1
Real number (ℝ≥0)

Distinct count200
Unique (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.12647058823529
Minimum0
Maximum199
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:26.363022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.95
Q167.75
median125
Q3184.25
95-th percentile199
Maximum199
Range199
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation62.73297726
Coefficient of variation (CV)0.5222244269
Kurtosis-1.253041787
Mean120.1264706
Median Absolute Deviation (MAD)58
Skewness-0.2393483363
Sum40843
Variance3935.426436
2020-08-25T01:50:26.467951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1995817.1%
 
6841.2%
 
11241.2%
 
19241.2%
 
19041.2%
 
18530.9%
 
8930.9%
 
9830.9%
 
11130.9%
 
4030.9%
 
13330.9%
 
14530.9%
 
15730.9%
 
16230.9%
 
7930.9%
 
18930.9%
 
19120.6%
 
10320.6%
 
6320.6%
 
2920.6%
 
5320.6%
 
14920.6%
 
9720.6%
 
18820.6%
 
14420.6%
 
Other values (175)21563.2%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1995817.1%
 
19810.3%
 
19710.3%
 
19610.3%
 
19510.3%
 
19410.3%
 
19310.3%
 
19241.2%
 
19120.6%
 
19041.2%
 

gain_ratio_2
Real number (ℝ≥0)

Distinct count194
Unique (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.71470588235294
Minimum0
Maximum193
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:26.587309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.95
Q167.75
median121
Q3180.25
95-th percentile193
Maximum193
Range193
Interquartile range (IQR)112.5

Descriptive statistics

Standard deviation60.97301803
Coefficient of variation (CV)0.5179728189
Kurtosis-1.227560707
Mean117.7147059
Median Absolute Deviation (MAD)56
Skewness-0.2542507328
Sum40023
Variance3717.708928
2020-08-25T01:50:26.701786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1936920.3%
 
9841.2%
 
15741.2%
 
10630.9%
 
9330.9%
 
15130.9%
 
14030.9%
 
9030.9%
 
8930.9%
 
16030.9%
 
18430.9%
 
18530.9%
 
7030.9%
 
12030.9%
 
3330.9%
 
11430.9%
 
3520.6%
 
14520.6%
 
7620.6%
 
5620.6%
 
14120.6%
 
3220.6%
 
4120.6%
 
4220.6%
 
13120.6%
 
Other values (169)20660.6%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1936920.3%
 
19210.3%
 
19110.3%
 
19010.3%
 
18910.3%
 
18810.3%
 
18710.3%
 
18610.3%
 
18530.9%
 
18430.9%
 

gain_ratio_3
Real number (ℝ≥0)

Distinct count191
Unique (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.12058823529412
Minimum0
Maximum190
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:26.815461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.95
Q170
median121.5
Q3181
95-th percentile190
Maximum190
Range190
Interquartile range (IQR)111

Descriptive statistics

Standard deviation59.61662317
Coefficient of variation (CV)0.5047098398
Kurtosis-1.181758308
Mean118.1205882
Median Absolute Deviation (MAD)55
Skewness-0.2950393502
Sum40161
Variance3554.141758
2020-08-25T01:50:26.917090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1906820.0%
 
18141.2%
 
8241.2%
 
7041.2%
 
13130.9%
 
9130.9%
 
13530.9%
 
14730.9%
 
15030.9%
 
12630.9%
 
11830.9%
 
11730.9%
 
5230.9%
 
1230.9%
 
9330.9%
 
10230.9%
 
8830.9%
 
7430.9%
 
18230.9%
 
18730.9%
 
7730.9%
 
17630.9%
 
11320.6%
 
3720.6%
 
7520.6%
 
Other values (166)20058.8%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1906820.0%
 
18910.3%
 
18810.3%
 
18730.9%
 
18620.6%
 
18520.6%
 
18420.6%
 
18320.6%
 
18230.9%
 
18141.2%
 

gain_ratio_4
Real number (ℝ≥0)

Distinct count203
Unique (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.43235294117648
Minimum0
Maximum202
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:27.037397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.95
Q171.75
median126
Q3189.25
95-th percentile202
Maximum202
Range202
Interquartile range (IQR)117.5

Descriptive statistics

Standard deviation64.13434103
Coefficient of variation (CV)0.5195910108
Kurtosis-1.243818856
Mean123.4323529
Median Absolute Deviation (MAD)59.5
Skewness-0.2594115571
Sum41967
Variance4113.213699
2020-08-25T01:50:27.148708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2026519.1%
 
7661.8%
 
19351.5%
 
10930.9%
 
17030.9%
 
9530.9%
 
8530.9%
 
7930.9%
 
18630.9%
 
15630.9%
 
19630.9%
 
12420.6%
 
10820.6%
 
1320.6%
 
920.6%
 
11220.6%
 
11420.6%
 
12020.6%
 
12220.6%
 
4420.6%
 
10420.6%
 
12520.6%
 
12620.6%
 
12920.6%
 
14020.6%
 
Other values (178)21262.4%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
920.6%
 
ValueCountFrequency (%) 
2026519.1%
 
20110.3%
 
20010.3%
 
19910.3%
 
19810.3%
 
19710.3%
 
19630.9%
 
19520.6%
 
19410.3%
 
19351.5%
 

gain_ratio_5
Real number (ℝ≥0)

Distinct count197
Unique (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.50882352941177
Minimum0
Maximum196
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:27.270158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q169.75
median124.5
Q3185
95-th percentile196
Maximum196
Range196
Interquartile range (IQR)115.25

Descriptive statistics

Standard deviation62.02723195
Coefficient of variation (CV)0.5147111235
Kurtosis-1.213831757
Mean120.5088235
Median Absolute Deviation (MAD)58.5
Skewness-0.2779808587
Sum40973
Variance3847.377503
2020-08-25T01:50:27.385131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1966920.3%
 
15030.9%
 
17430.9%
 
7930.9%
 
7830.9%
 
18330.9%
 
12230.9%
 
18530.9%
 
10030.9%
 
3330.9%
 
14830.9%
 
12620.6%
 
13520.6%
 
10220.6%
 
10320.6%
 
10520.6%
 
10920.6%
 
11020.6%
 
13920.6%
 
13720.6%
 
11220.6%
 
12520.6%
 
11320.6%
 
11420.6%
 
11720.6%
 
Other values (172)21362.6%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
820.6%
 
910.3%
 
ValueCountFrequency (%) 
1966920.3%
 
19510.3%
 
19410.3%
 
19310.3%
 
19210.3%
 
19120.6%
 
19020.6%
 
18920.6%
 
18820.6%
 
18710.3%
 

gain_ratio_6
Real number (ℝ≥0)

Distinct count195
Unique (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.86764705882354
Minimum0
Maximum194
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:27.516832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.95
Q170.75
median128.5
Q3189.25
95-th percentile194
Maximum194
Range194
Interquartile range (IQR)118.5

Descriptive statistics

Standard deviation62.1786617
Coefficient of variation (CV)0.5102146731
Kurtosis-1.230155255
Mean121.8676471
Median Absolute Deviation (MAD)59
Skewness-0.3212666121
Sum41435
Variance3866.185971
2020-08-25T01:50:27.627918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1948123.8%
 
8341.2%
 
7841.2%
 
9530.9%
 
9030.9%
 
16630.9%
 
16330.9%
 
18130.9%
 
6530.9%
 
12830.9%
 
13230.9%
 
1330.9%
 
11920.6%
 
11820.6%
 
11520.6%
 
13020.6%
 
11420.6%
 
10920.6%
 
13320.6%
 
13520.6%
 
10820.6%
 
10220.6%
 
13620.6%
 
10120.6%
 
8720.6%
 
Other values (170)19858.2%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
920.6%
 
ValueCountFrequency (%) 
1948123.8%
 
19310.3%
 
19210.3%
 
19110.3%
 
19010.3%
 
18910.3%
 
18810.3%
 
18710.3%
 
18610.3%
 
18510.3%
 

gain_ratio_7
Real number (ℝ≥0)

Distinct count191
Unique (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.85882352941177
Minimum0
Maximum190
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:27.939351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.95
Q174
median128.5
Q3186.25
95-th percentile190
Maximum190
Range190
Interquartile range (IQR)112.25

Descriptive statistics

Standard deviation59.52231736
Coefficient of variation (CV)0.4884530774
Kurtosis-1.132619489
Mean121.8588235
Median Absolute Deviation (MAD)56.5
Skewness-0.3851877
Sum41432
Variance3542.906264
2020-08-25T01:50:28.039940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1908123.8%
 
14441.2%
 
7441.2%
 
9030.9%
 
11030.9%
 
15630.9%
 
18330.9%
 
11330.9%
 
12130.9%
 
18030.9%
 
7830.9%
 
13730.9%
 
10530.9%
 
16230.9%
 
12630.9%
 
14120.6%
 
10620.6%
 
13220.6%
 
14320.6%
 
14520.6%
 
14720.6%
 
14920.6%
 
10220.6%
 
13120.6%
 
3720.6%
 
Other values (166)19557.4%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1908123.8%
 
18920.6%
 
18810.3%
 
18710.3%
 
18610.3%
 
18520.6%
 
18410.3%
 
18330.9%
 
18210.3%
 
18110.3%
 

gain_ratio_8
Real number (ℝ≥0)

Distinct count188
Unique (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.79117647058824
Minimum0
Maximum187
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:28.156780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q167.75
median127.5
Q3184.25
95-th percentile187
Maximum187
Range187
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation60.38002008
Coefficient of variation (CV)0.5082870788
Kurtosis-1.248399058
Mean118.7911765
Median Absolute Deviation (MAD)58.5
Skewness-0.3481961852
Sum40389
Variance3645.746825
2020-08-25T01:50:28.259580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1878324.4%
 
17851.5%
 
17741.2%
 
12030.9%
 
7530.9%
 
13930.9%
 
8430.9%
 
6430.9%
 
9730.9%
 
17130.9%
 
15330.9%
 
10630.9%
 
18030.9%
 
12830.9%
 
13030.9%
 
8130.9%
 
11320.6%
 
7920.6%
 
10420.6%
 
12120.6%
 
10120.6%
 
9820.6%
 
12520.6%
 
8620.6%
 
13320.6%
 
Other values (163)19156.2%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1878324.4%
 
18610.3%
 
18510.3%
 
18410.3%
 
18310.3%
 
18210.3%
 
18120.6%
 
18030.9%
 
17910.3%
 
17851.5%
 

gain_ratio_9
Real number (ℝ≥0)

Distinct count188
Unique (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.6029411764706
Minimum0
Maximum187
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:28.375401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.95
Q168.75
median127.5
Q3187
95-th percentile187
Maximum187
Range187
Interquartile range (IQR)118.25

Descriptive statistics

Standard deviation60.26297819
Coefficient of variation (CV)0.5038586643
Kurtosis-1.215137729
Mean119.6029412
Median Absolute Deviation (MAD)59.5
Skewness-0.3663216678
Sum40665
Variance3631.62654
2020-08-25T01:50:28.481055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1878825.9%
 
17841.2%
 
3830.9%
 
11930.9%
 
14330.9%
 
9430.9%
 
8530.9%
 
12830.9%
 
14130.9%
 
17630.9%
 
13530.9%
 
10420.6%
 
10320.6%
 
10020.6%
 
10120.6%
 
14920.6%
 
9920.6%
 
6020.6%
 
5120.6%
 
3320.6%
 
10520.6%
 
3420.6%
 
13920.6%
 
6120.6%
 
10720.6%
 
Other values (163)19356.8%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
420.6%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1878825.9%
 
18610.3%
 
18510.3%
 
18410.3%
 
18310.3%
 
18210.3%
 
18120.6%
 
18010.3%
 
17920.6%
 
17841.2%
 

gain_ratio_10
Real number (ℝ≥0)

Distinct count196
Unique (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.01764705882353
Minimum0
Maximum195
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:28.604064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.95
Q173.75
median135.5
Q3195
95-th percentile195
Maximum195
Range195
Interquartile range (IQR)121.25

Descriptive statistics

Standard deviation62.71116291
Coefficient of variation (CV)0.4976379449
Kurtosis-1.15294356
Mean126.0176471
Median Absolute Deviation (MAD)59.5
Skewness-0.4314636148
Sum42846
Variance3932.689953
2020-08-25T01:50:28.716153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1958825.9%
 
12651.5%
 
12341.2%
 
14730.9%
 
15930.9%
 
5930.9%
 
18630.9%
 
12930.9%
 
8530.9%
 
15730.9%
 
15020.6%
 
15220.6%
 
13320.6%
 
3420.6%
 
14220.6%
 
2920.6%
 
14020.6%
 
3820.6%
 
13520.6%
 
15620.6%
 
14620.6%
 
4620.6%
 
13020.6%
 
11920.6%
 
11120.6%
 
Other values (171)19256.5%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
420.6%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1958825.9%
 
19410.3%
 
19310.3%
 
19210.3%
 
19110.3%
 
19010.3%
 
18920.6%
 
18810.3%
 
18720.6%
 
18630.9%
 

gain_ratio_11
Real number (ℝ≥0)

Distinct count199
Unique (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.78529411764706
Minimum0
Maximum198
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2020-08-25T01:50:28.844800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.95
Q171
median131
Q3196.25
95-th percentile198
Maximum198
Range198
Interquartile range (IQR)125.25

Descriptive statistics

Standard deviation63.05972555
Coefficient of variation (CV)0.5013282832
Kurtosis-1.210244227
Mean125.7852941
Median Absolute Deviation (MAD)62
Skewness-0.3382325917
Sum42767
Variance3976.528987
2020-08-25T01:50:28.951667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1988424.7%
 
6141.2%
 
19341.2%
 
10841.2%
 
12930.9%
 
11130.9%
 
12730.9%
 
10430.9%
 
5430.9%
 
8830.9%
 
13920.6%
 
13720.6%
 
5520.6%
 
14520.6%
 
14620.6%
 
14720.6%
 
14820.6%
 
15020.6%
 
12620.6%
 
5120.6%
 
5320.6%
 
13120.6%
 
11620.6%
 
11820.6%
 
5820.6%
 
Other values (174)19657.6%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
1988424.7%
 
19710.3%
 
19610.3%
 
19510.3%
 
19410.3%
 
19341.2%
 
19210.3%
 
19110.3%
 
19020.6%
 
18920.6%
 

sex
Boolean

Distinct count2
Unique (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1
203
0
137
ValueCountFrequency (%) 
120359.7%
 
013740.3%
 

target.1
Boolean

Distinct count2
Unique (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
177
1
163
ValueCountFrequency (%) 
017752.1%
 
116347.9%
 

Interactions

2020-08-25T01:50:03.017459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:03.141488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:03.278596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:03.414062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:03.546551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:03.682893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:03.817627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:03.954561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:04.087865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:04.220851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:04.351615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:04.486926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:04.621106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:04.761725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:04.910169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:05.061906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:05.211913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:05.364313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:05.516244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:05.864583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:06.008360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:06.158121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:06.310011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:06.463497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:06.621455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:06.760400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:06.909489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:07.059799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:07.210751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:07.365757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:07.518567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:07.669147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:07.819640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:07.966034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:08.111832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:08.264383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:08.413759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:08.546148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:08.690762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:08.839906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:08.983726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:09.134054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:09.280656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:09.427016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:09.568347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:09.710560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:09.855556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:10.003564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:10.365213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:10.503119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:10.654717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:10.811936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:10.963350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:11.114861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:11.266782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:11.420616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:11.572013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:11.718641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:11.867572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:12.020984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:12.170520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:12.312253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:12.466465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:12.618151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:12.766243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:12.919651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:13.072907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:13.222126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:13.370711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:13.516604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:13.662214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:13.812552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:13.969290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:14.110156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:14.260381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:14.415866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:14.561574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:14.921709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:15.078108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:15.228492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:15.381745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:15.537506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:15.690159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:15.848040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:15.999670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:16.132813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:16.278787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:16.426027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:16.573280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:16.721864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:16.869689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:17.018426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:17.164831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:17.309042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:17.455848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:17.612165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:17.767912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:17.910378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:18.057402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:18.201333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:18.341540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:18.487981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:18.636477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:18.781820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:18.928063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:19.072487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:19.436696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:19.586029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:19.735386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:19.868087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:20.016521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:20.162261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:20.302999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:20.448949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:20.603633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:20.751369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:20.893533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:21.038484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:21.185759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:21.337332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:21.489392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:21.629689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:21.788610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:21.942818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:22.091660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:22.240454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:22.392779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:22.549196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:22.696764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:22.843380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:22.993390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:23.146204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:23.295342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:23.432390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:23.587429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:23.945146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:24.099642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:24.251117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:24.403505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:24.570864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:24.720520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:24.869056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:25.018075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:25.174143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:50:29.095396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:50:29.386136image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:50:29.672710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:50:29.961009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:50:25.459126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:25.810362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

IDtargetgain_ratio_1gain_ratio_2gain_ratio_3gain_ratio_4gain_ratio_5gain_ratio_6gain_ratio_7gain_ratio_8gain_ratio_9gain_ratio_10gain_ratio_11sextarget.1
07.0116716217715717917517212114315913900
17.01150921291369116013713014314113500
27.0011711716020211014311310745818800
37.02199193937619665659965824900
47.0112811885244764124981285712000
519.0115712619015413383917516516618900
619.01110761028812495190711089112700
719.0016176506945362710611913211300
819.01891306012979138136108529814600
920.01104100921059446648146458500

Last rows

IDtargetgain_ratio_1gain_ratio_2gain_ratio_3gain_ratio_4gain_ratio_5gain_ratio_6gain_ratio_7gain_ratio_8gain_ratio_9gain_ratio_10gain_ratio_11sextarget.1
330267.02546910210790100826354948411
331274.001991931909212615819018718719519811
332275.0118614012415811419413812810119510011
333275.001871191471401671241901871381958611
334275.01118144998314777471191871019111
335275.02199607670196194911053119519811
336276.019012037124109853984187962311
337276.0076271131295786133125817911011
338276.01167124841348813912612618713916811
339276.027311482116127108114941051627111